MMP-13 stimulates osteoclast differentiation and activation in tumour breast bone metastases

INTRODUCTION: The increased bone degradation in osteolytic metastases depends on stimulation of mature osteoclasts and on continuous differentiation of new pre-osteoclasts. Metalloproteinases (MMP)-13 is expressed in a broad range of primary malignant tumours and it is emerging as a novel biomarker. Recent data suggest a direct role of MMP-13 in dissolving bone matrix complementing the activity of MMP-9 and other enzymes. Tumour-microenvironment interactions alter gene expression in malignant breast tumour cells promoting osteolytic bone metastasis. Gene expression profiles revealed that MMP-13 was among the up-regulated genes in tumour-bone interface and its abrogation reduced bone erosion. The precise mechanism remained not fully understood. Our purpose was to further investigate the mechanistic role of MMP-13 in bone osteolytic lesions. METHODS: MDA-MB-231 breast cancer cells that express MMP-13 were used as a model for in vitro and in vivo experiments. Conditioned media from MDA-MB-231 cells were added to peripheral blood mononuclear cultures to monitor pre-osteoclast differentiation and activation. Bone erosion was evaluated after injection of MMP-13-silenced MDA-MB-231 cells into nude mice femurs. RESULTS: MMP-13 was co-expressed by human breast tumour bone metastases with its activator MT1-MMP. MMP-13 was up-regulated in breast cancer cells after in vitro stimulation with IL-8 and was responsible for increased bone resorption and osteoclastogenesis, both of which were reduced by MMP inhibitors. We hypothesized that MMP-13 might be directly involved in the loop promoting pre-osteoclast differentiation and activity. We obtained further evidence for a direct role of MMP-13 in bone metastasis by a silencing approach: conditioned media from MDA-MB-231 after MMP-13 abrogation or co-cultivation of silenced cells with pre-osteoclast were unable to increase pre-osteoclast differentiation and resorption activity. MMP-13 activated pre-MMP-9 and promoted the cleavage of galectin-3, a suppressor of osteoclastogenesis, thus contributing to pre-osteoclast differentiation. Accordingly, MMP-13 abrogation in tumour cells injected into the femurs of nude mice reduced the differentiation of TRAP positive cells in bone marrow and within the tumour mass as well as bone erosion. CONCLUSIONS: These results indicate that within the inflammatory bone microenvironment MMP-13 production was up-regulated in breast tumour cells leading to increased pre-osteoclast differentiation and their subsequent activation

Dermoscopic features of a primary scalp melanoma and its cutaneous metastases

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Additional value of volumetric and texture analysis on FDG PET assessment in paediatric Hodgkin lymphoma: an Italian multicentric study protocol

Introduction Assessment of response to therapy in paediatric patients with Hodgkin lymphoma (HL) by 18F-fluorodeoxyglucose positron emission tomography/CT has become a powerful tool for the discrimination of responders from non-responders. The addition of volumetric and texture analyses can be regarded as a valuable help for disease prognostication and biological characterisation. Based on these premises, the Hodgkin Lymphoma Study Group of the Associazione Italiana Ematologia Oncologia Pediatrica (AIEOP) has designed a prospective evaluation of volumetric and texture analysis in the Italian cohort of patients enrolled in the EuroNet-PHL-C2.Methods and analysis The primary objective is to compare volumetric assessment in patiens with HL at baseline and during the course of therapy with standard visual and semiquantitative analyses. The secondary objective is to identify the impact of volumetric and texture analysis on bulky masses. The tertiary objective is to determine the additional value of multiparametric assessment in patients having a partial response on morphological imaging.The overall cohort of the study is expected to be round 400–500 patients, with approximately half presenting with bulky masses. All PET scans of the Italian cohort will be analysed for volumetric assessment, comprising metabolic tumour volume and total lesion glycolysis at baseline and during the course of therapy. A dedicated software will delineate semiautomatically contours using different threshold methods, and the impact of each segmentation techniques will be evaluated. Bulky will be defined on contiguous lymph node masses ≥200 mL on CT/MRI. All bulky masses will be outlined and analysed by the same software to provide textural features. Morphological assessment will be based on RECIL 2017 for response definition.Ethics and dissemination The current study has been ethically approved (AIFA/SC/P/27087 approved 09/03/2018; EudraCT 2012-004053-88, EM-04). The results of the different analyses performed during and after study completion the will be actively disseminated through peer-reviewed journals, conference presentations, social media, print media and internet

Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model

The aim of this study is to predict local failure after partial prostate re-irradiation for the treatment of isolated locally recurrent prostate cancer by using a machine learning classifier based on radiomic features from pre-treatment computed tomography (CT), positron-emission tomography (PET) and biological effective dose distribution (BED) of the radiotherapy plan. The analysis was conducted on a monocentric dataset of 43 patients with evidence of isolated intraprostatic recurrence of prostate cancer after primary external beam radiotherapy. All patients received partial prostate re-irradiation delivered by volumetric modulated arc therapy. The gross tumor volume (GTV) of each patient was manually contoured from planning CT, choline-PET and dose maps. An ensemble machine learning pipeline including unbalanced data correction and feature selection was trained using the radiomic and dosiomic features as input for predicting occurrence of local failure. The model performance was assessed using sensitivity, specificity, accuracy and area under receiver operating characteristic curves of the score function in 10-fold cross validation repeated 100 times. Local failure was observed in 13 patients (30%), with a median time to recurrence of 36.7 months (range = 6.1–102.4 months). A four variables ensemble machine learning model resulted in accuracy of 0.62 and AUC 0.65. According to our results, a dosiomic machine learning classifier can predict local failure after partial prostate re-irradiation

Adjuvant radiotherapy and radioiodine treatment for locally advanced differentiated thyroid cancer: systematic review and meta-analysis

Background: Treatment for locally advanced differentiated thyroid cancer is surgery followed by radioiodine while the role of adjuvant external beam radiotherapy (EBRT) is debated. Methods: The panel of the Italian Association of Radiotherapy and Clinical Oncology developed a clinical recommendation on the addition of EBRT to radioiodine after surgery for locally advanced differentiated thyroid cancer by using the Grades of Recommendation, Assessment, Development, and Evaluation methodology and the Evidence to Decision framework. A systematic review with meta-analysis about this topic was conducted with a focus on outcome of benefits and toxicity. Results: Locoregional control was improved by EBRT while no considerable toxicity impact was reported. Conclusion: The panel judged uncertain the benefit/harms balance; final recommendation was conditional both for EBRT + radioiodine and radioiodine alone in the adjuvant setting